Author Identifier (ORCID)
Lai Chang Zhang: https://orcid.org/0000-0003-0661-2051
Abstract
In this study, a Ti1.5Nb1Ta0.5Zr1Mo0.5 (TNTZM) high-entropy alloy was fabricated using laser powder bed fusion (LPBF). By integrating 63 sets of parameter trials with machine learning (ML) models, an optimised process window was identified, achieving a density of up to 99.9%. The combination of relatively high laser power and low scanning speed resulted in the formation of a stable cellular structure. Subsequent heat treatments at 700, 850, and 1000°C showed that while small-angle misorientations developed at cell-wall interfaces and medium-entropy (Ti–Zr–Mo) second-phase particles precipitated preferentially in the cell walls, the overall cellular architecture remained intact. Mechanical testing showed that these heat-treated samples exhibited yield strengths over 150 MPa higher than the as-built samples, while still retaining nearly 50% ductility under short-term heat treatment. In particular, small-angle grain boundaries and nanoscale second-phase particles together reinforce the cell walls and promote intracellular dislocation accumulation, thereby improving the overall mechanical properties of the alloy. These results demonstrate that combining ML-guided process design with targeted heat treatment is an effective method for additive manufacturing of refractory HEAs with high density and mechanical properties.
Keywords
Additive manufacture, cellular structure, heat treatment, machine learning, refractory high-entropy alloys
Document Type
Journal Article
Date of Publication
1-1-2025
Volume
20
Issue
1
Publisher
Taylor & Francis
School
Centre for Advanced Materials and Manufacturing / School of Engineering
RAS ID
83444
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
Jiang, D., Luo, M., Liu, C., Zhang, Y., Zhang, L., Wang, K., Wang, W., Xie, L., Wang, L., Lu, W., & Zhang, D. (2025). 3D Printing parameter optimisation combined with heat treatment for achieving high density and enhanced performance in refractory high-entropy alloys. Virtual and Physical Prototyping, 20(1). https://doi.org/10.1080/17452759.2025.2524524